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\(\sigma \)GTTM III: Learning-Based Time-Span Tree Generator Based on PCFG

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Book cover Music, Mind, and Embodiment (CMMR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9617))

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Abstract

An automatic analyzer based on the generative theory of tonal music (GTTM) for acquiring a time-span tree is described. Although an analyzer based on GTTM was previously reported, it requires manually manipulating 46 adjustable parameters on a computer screen in order to analyze a time-span tree properly. We reformalized the time-span reduction in GTTM on the basis of a probabilistic model called probabilistic context-free grammar, which enables acquiring the most likely time-span tree. Applying leave-one-out cross validation over 300 datasets revealed that the new analyzer outperformed our previously developed GTTM analyzer.

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Notes

  1. 1.

    Although the theory accepts homophonic music, we first restricted our target music to monophonic music. Recently, we attempted to develop a time-span tree analyzer for polyphonic music [7].

  2. 2.

    Although we can use the Viterbi algorithm, in the experiment we did a full search. In future we plan to implement the Viterbi algorithm to reduce the computing time.

  3. 3.

    When the user stops moving the iPhone/iPod Touch, the unit plays the backing melody of “The Other Day, I Met a Bear (The Bear Song)”. When the user shakes it vigorously, it plays heavy soloing. When the user shakes it slowly, it plays a morphed melody between the backing and heavy soloing. The ShakeGuitar can be downloaded at http://gttm.jp/hamanaka/en/shakeguitar/.

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Acknowledgments

This work was supported in part by JSPS KAKENHI Grant Numbers 23500145, 25330434, and 25700036 and PRESTO, JST.

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Authors and Affiliations

  1. Kyoto University, Kyoto, 606-8501, Japan

    Masatoshi Hamanaka

  2. Future University Hakodate, Hakodate, Japan

    Keiji Hirata

  3. JAIST - Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan

    Satoshi Tojo

Authors
  1. Masatoshi Hamanaka
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  2. Keiji Hirata
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  3. Satoshi Tojo
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Corresponding author

Correspondence to Masatoshi Hamanaka .

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Editors and Affiliations

  1. CNRS - LMA, Marseille Cedex 20, France

    Richard Kronland-Martinet

  2. CNRS - LMA, Marseille, France

    Mitsuko Aramaki

  3. CNRS - LMA, Marseille, France

    Sølvi Ystad

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Hamanaka, M., Hirata, K., Tojo, S. (2016). \(\sigma \)GTTM III: Learning-Based Time-Span Tree Generator Based on PCFG. In: Kronland-Martinet, R., Aramaki, M., Ystad, S. (eds) Music, Mind, and Embodiment. CMMR 2015. Lecture Notes in Computer Science(), vol 9617. Springer, Cham. https://doi.org/10.1007/978-3-319-46282-0_25

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  • DOI: https://doi.org/10.1007/978-3-319-46282-0_25

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  • Online ISBN: 978-3-319-46282-0

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